AUTOMATISE: Multiple Aspect Trajectory Data Mining Tool Library
| Autor: | Tarlis Tortelli Portela, Vania Bogorny, Anna Bernasconi, Chiara Renso |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | MDM 2022-23rd IEEE International Conference on Mobile Data Management, pp. 282–285, Paphos, Cyprus, Online, 6-9/06/2022 2022 23rd IEEE International Conference on Mobile Data Management (MDM) |
| DOI: | 10.1109/mdm55031.2022.00060 |
| Popis: | ith the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings. |
| Databáze: | OpenAIRE |
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